Exchange rate forecasting using modified empirical mode decomposition and least squares support vector machine
Forecasting exchange rate requires a model that can capture the non-stationary and non-linearity of the exchange rate data. In this paper, empirical mode decomposition (EMD) is combines with least squares support vector machine (LSSVM) model in order to forecast daily USD/TWD exchange rate. EMD is u...
Saved in:
Main Authors: | Abdul Rashid, Nur Izzati, Samsudin, Ruhaidah, Shabri, Ani |
---|---|
Format: | Article |
Published: |
International Center for Scientific Research and Studies
2016
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/71230/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010427055&partnerID=40&md5=631643f0150ef2ece9a4bf0e24623da6 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Empirical mode decomposition-least squares support vector machine based for water demand forecasting
by: Shabri, Ani, et al.
Published: (2015) -
Tourism forecasting using hybrid modified empirical mode decomposition and neural network
by: Yahya, Nurhaziyatul Adawiyah, et al.
Published: (2017) -
Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand
by: Samsudin, Ruhaidah, et al.
Published: (2010) -
A hybrid GMDH and least squares support vector machines in time series forecasting
by: Samsudin, Ruhaidah, et al.
Published: (2011) -
A hybrid least squares support vector machines and GMDH approach for river flow forecasting
by: Samsudin, Ruhaidah, et al.
Published: (2010)